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Recently there has been increased interest in semi-supervised classification in the presence of graphical information.
Stochastic blockmodels: First steps
P. W. Holland, K. B. Laskey, and S. Leinhardt · 1983
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Gaussian inequalities
R J Adler and J E Taylor · 2007
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Graph implementations for nonsmooth convex programs, recent advances in learning and control (a tribute to M. Vidyasagar)
V. Blondel, S. Boyd, and H. Kimura · 2008
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Clustering large attributed graphs: A balance between structural and attribute similarities
H. Cheng, Y. Zhou, and J. X. Yu · 2011
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Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
A. Decelle, F. Krzakala, C. Moore, and L. Zdeborová · 2011
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Community detection based on structural and attribute similarities
T. A. Dang and E. Viennet · 2012
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Graph embedding in vector spaces by node attribute statistics
J. Gilbert, E. Valveny, and H. Bunke · 2012
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CVX: Matlab software for disciplined convex programming, version 2.0 beta
M. Grant and S. Boyd · 2013
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Spectral subspace clustering for graphs with feature vectors
S. Günnemann, I Färber, S. Raubach, and T. Seidl · 2013
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Community detection in networks with node attributes
J. Yang, J. McAuley, and J. Leskovec · 2013
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Community detection thresholds and the weak ramanujan property
Laurent Massoulié · 2014
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Exact recovery in the stochastic block model
E. Abbe, A. S. Bandeira, and G. Hall · 2015
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Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery
E. Abbe and C. Sandon · 2015
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Non-backtracking spectrum of random graphs: community detection and non-regular ramanujan graphs
C. Bordenave, M. Lelarge, and L. Massoulié · 2015
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Asymptotic mutual information for the two-groups stochastic block model
Y. Deshpande, E. Abbe, and A. Montanari · 2015
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Consistency thresholds for the planted bisection model
E. Mossel, J. Neeman, and A. Sly · 2015
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Information-theoretic thresholds for community detection in sparse networks
J. Banks, C. Moore, J. Neeman, and P. Netrapalli · 2016
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Interaction Networks for Learning about Objects, Relations and Physics
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. Kavukcuoglu · 2016
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A proof of the block model threshold conjecture
E. Mossel, J. Neeman, and A. Sly · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
R. Vershynin · 2018
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Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
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Supervised community detection with line graph neural networks
Z. Chen, L. Li, and J. Bruna · 2019
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Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
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Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks
D. Jin, Z. Liu, W. Li, D. He, and W. Zhang · 2019
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A. Montanari and S. Sen · 2016
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Covariate-assisted spectral clustering
N. Binkiewicz, J. T. Vogelstein, and K. Rohe · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Cited alongside, same era.
The computer science and physics of community detection: Landscapes, phase transitions, and hardness
C. Moore · 2017
Cited alongside, same era.
Community detection and stochastic block models: Recent developments
E. Abbe · 2018
Cited alongside, same era.
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Stochastic blockmodels meet graph neural networks
N. Mehta, C. L. Duke, and P. Rai · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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Generalization and representational limits of graph neural networks
V. Garg, S. Jegelka, and T. Jaakkola · 2020
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Graph representation learning
L. W. Hamilton · 2020
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How hard is to distinguish graphs with graph neural networks?
A. Loukas · 2020
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What graph neural networks cannot learn: Depth vs width
A. Loukas · 2020
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks, 2021
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra · 2021
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